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Articles from Karen Grace-Martin | Selfgrowth.com
- Assessing The Fit Of Regression Models - by Submitted on Sep 25, 2008 from Karen Grace-MartinA well-fitting regression model results in predicted values close to the observed data values. The mean model, which uses the mean for every predicted value, generally would be used if there were no informative predictor variables. The fit of a proposed regression model should therefore be ...
Views: 6509(1 vote) - Logistic Regression Models for Multinomial and Ordinal Variables - by Submitted on Jan 28, 2009 from Karen Grace-MartinMultinomial Logistic Regression The multinomial (a.k.a. polytomous) logistic regression model is a simple extension of the binomial logistic regression model. They are used when the dependent variable has more than two nominal (unordered) categories. Dummy coding of independent variables is ...
Views: 2974(1 vote) - Centering Variables to Reduce Multicollinearity - by Submitted on Nov 24, 2008 from Karen Grace-MartinCentering is one of those topics in statistics that everyone seems to have heard of, but most people don’t know much about. It has developed a mystique that is entirely unnecessary. Centering just means subtracting a single value from all of your data points. It shifts the scale of a ...
Views: 15561(1 vote) - Circular Statistics - by Submitted on Oct 12, 2008 from Karen Grace-MartinCircular variables, which indicate direction or cyclical time, can be of great interest to biologists, geographers, and social scientists. The defining characteristic of circular variables is that the beginning and end of their scales meet. For example, compass direction is often defined with ...
Views: 2112 - Concepts in Linear Regression You Need to Know Before Learning Multilevel Models - by Submitted on Mar 08, 2009 from Karen Grace-MartinIt seems very many researchers are needing to learn multilevel and mixed models, and I have to say, it's not so easy on your own. I too went to graduate school before it was taught in classes--we did learn mixed models as in Split Plot designs, but things have progressed a bit since then. So I ...
Views: 1022 - Five Steps for Calculating Sample Size - by Submitted on Nov 17, 2008 from Karen Grace-MartinNearly all granting agencies require an estimate of an adequate sample size to detect the effects hypothesized in the study. But all studies are well served by estimates of sample size, as it can save a great deal on resources. Why? Undersized studies can't find real results and oversized ...
Views: 2390 - Interpreting Interactions In Regression - by Submitted on Sep 28, 2008 from Karen Grace-MartinAdding interaction terms to a regression model can greatly expand understanding of the relationships among the variables in the model and allows more hypotheses to be tested. A previous article, Interpreting Regression Coefficients, discussed how to interpret coefficients in regression models. ...
Views: 1115 - Introduction To Logistic Regression - by Submitted on Oct 12, 2008 from Karen Grace-MartinResearchers are often interested in setting up a model to analyze the relationship between some predictors (i.e., independent variables) and a response (i.e., dependent variable). Linear regression is commonly used when the response variable is continuous. One assumption of linear models is ...
Views: 844 - Limitations Of Common Solutions To Missing Data - by Submitted on Oct 12, 2008 from Karen Grace-MartinA previous article discussed some of the causes of missing data and some of the consequences of analyzing only complete cases. This newsletter will discuss some other common ways of dealing with missing data, with a discussion of their advantages and disadvantages. Available case analysis ...
Views: 843 - Linear Regression - Interpreting Lower Order Coefficients When the Model Contains an Interaction - by Submitted on Mar 08, 2009 from Karen Grace-MartinA Linear Regression Model with an interaction between two predictors (X1 and X2) has the form: Y = B0 + B1X1 + B2X2 + B3X1*X2. It doesn't really matter if X1 and X2 are categorical or continuous, but let's assume they are continuous for simplicity. One important concept is that B1 ...
Views: 1107 - Linear Regression Analysis - Centering a Covariate to Improve Interpretability - by Submitted on Mar 08, 2009 from Karen Grace-MartinThe reason for centering a continuous covariate is that it can improve interpretability. For example, say you had one categorical predictor with 4 categories and one continuous covariate, plus an interaction between them. First, you'll notice that if you center your covariate at the mean, ...
Views: 2410 - Missing Data Mechanisms - by Submitted on Oct 12, 2008 from Karen Grace-MartinAs almost any researcher can attest, missing data are a widespread problem. Data from surveys, experiments, and secondary sources are often missing some data. The impact of the missing data on the results of statistical analysis depends on the mechanism which caused the data to be missing and ...
Views: 793 - Multilevel Models with Crossed Random Effects - by Submitted on Dec 21, 2008 from Karen Grace-MartinMost researchers know by now to use Mixed Models when observations are clustered. Examples include studies in which patients share the same doctor, plants grow in the same field, or multiple responses are observed from the same study participant. The observations at Level 1 (patient, plant, ...
Views: 1632 - Plan Your Data Analysis in 3 Steps - by Submitted on Nov 24, 2008 from Karen Grace-MartinBack when I was doing psychology research, one of my biggest challenges was having enough data. And while this is probably still true for many experimental researchers, with the internet’s ability to make data sets accessible, it seems data is reproducing faster than rabbits. Now I see ...
Views: 1350 - Recommended Solutions To Missing Data - by Submitted on Oct 12, 2008 from Karen Grace-MartinThere are two methods for dealing with missing data that have become available in mainstream statistical software in the last few years. These two methods are vast improvements over traditional approaches, as described in Limitations to Common Approaches to Missing Data. This article outlines ...
Views: 1546 - Specifying Fixed And Random Factors In Mixed Or Multi-Level Models - by Submitted on Sep 28, 2008 from Karen Grace-MartinSince SAS introduced Proc Mixed about fifteen years ago, S-Plus, Stata and SPSS have implemented procedures to analyze mixed models, greatly broadening the options available to researchers. These programs require correctly specifying the fixed and random factors of the model to obtain accurate ...
Views: 953 - Statistical Consulting 101: 4 Questions You Must Answer to Choose the Appropriate Statistical Method - by Submitted on Mar 04, 2009 from Karen Grace-MartinOne of the most common situations in which researchers get stuck with statistics is choosing which statistical methodology is appropriate to analyze their data. If you start by asking the following four questions, you will be able to narrow things down considerably. Even if you don’t know the ...
Views: 819 - The Many Names Of Independent Variables - by Submitted on Sep 25, 2008 from Karen Grace-MartinStatistical models, such as general linear models (linear regression, ANOVA, mixed models) and generalized linear models (logistic, Poisson, proportional hazard regression, etc.) all have the same general form. On the left side of the equation is one or more response variables, Y. On the right ...
Views: 1681 - Why ANOVA and Linear Regression are the Same Analysis - by Submitted on Mar 14, 2009 from Karen Grace-MartinIf your graduate statistical training was anything like mine, you learned ANOVA in one class and Linear Regression in another. My professors would often say things like "ANOVA is just a special case of Regression," then do a lot of hand waving when pressed to explain. It was not until I started ...
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